BeMatch: a platform for matchmaking service behavior models
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Behavioral analysis of web services for supporting mediated service interoperations
Proceedings of the 10th international conference on Electronic commerce
Recommendation Based Process Modeling Support: Method and User Experience
ER '08 Proceedings of the 27th International Conference on Conceptual Modeling
Efficient and accurate retrieval of business process models through indexing
OTM'10 Proceedings of the 2010 international conference on On the move to meaningful internet systems - Volume Part I
Querying business process models based on semantics
DASFAA'11 Proceedings of the 16th international conference on Database systems for advanced applications: Part II
Efficient retrieval of similar business process models based on structure
OTM'11 Proceedings of the 2011th Confederated international conference on On the move to meaningful internet systems - Volume Part I
Efficient querying of large process model repositories
Computers in Industry
Querying business process model repositories
World Wide Web
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The existing service discovery infrastructure with UDDI as the de facto standard, is limited in that it does not support more complex searching based on matching business processes. Two business processes match if they agree on their simple services, theirprocessesing order as well as any mandatory or optional requirements for the service. This matching semanctics can be formalized by modelling business processes as annotated finite state autamata (aFSAs) and deciding emptiness of intersection aFSA. Computing the intersection of aFSAs and deciding emptiness are computationally expensive, being more than quadratic on the number of states and transistions, thusdoes not scale for large service repositories. This paper presents an approach for indexing and matching business processes modeled as aFSAs, for the purpose of service directory. Evaluation of this approach shows a preformance gain of several orders of magnitude over sequential matching and linear complexity with regard to the data size.